55,014 research outputs found
Single-machine scheduling with stepwise tardiness costs and release times
We study a scheduling problem that belongs to the yard operations component of the railroad planning problems, namely the hump sequencing problem. The scheduling problem is characterized as a single-machine problem with stepwise tardiness cost objectives. This is a new scheduling criterion which is also relevant in the context of traditional machine scheduling problems. We produce complexity results that characterize some cases of the problem as pseudo-polynomially solvable. For the difficult-to-solve cases of the problem, we develop mathematical programming formulations, and propose heuristic algorithms. We test the formulations and heuristic algorithms on randomly generated single-machine scheduling problems and real-life datasets for the hump sequencing problem. Our experiments show promising results for both sets of problems
On the Complexity of -Closeness Anonymization and Related Problems
An important issue in releasing individual data is to protect the sensitive
information from being leaked and maliciously utilized. Famous privacy
preserving principles that aim to ensure both data privacy and data integrity,
such as -anonymity and -diversity, have been extensively studied both
theoretically and empirically. Nonetheless, these widely-adopted principles are
still insufficient to prevent attribute disclosure if the attacker has partial
knowledge about the overall sensitive data distribution. The -closeness
principle has been proposed to fix this, which also has the benefit of
supporting numerical sensitive attributes. However, in contrast to
-anonymity and -diversity, the theoretical aspect of -closeness has
not been well investigated.
We initiate the first systematic theoretical study on the -closeness
principle under the commonly-used attribute suppression model. We prove that
for every constant such that , it is NP-hard to find an optimal
-closeness generalization of a given table. The proof consists of several
reductions each of which works for different values of , which together
cover the full range. To complement this negative result, we also provide exact
and fixed-parameter algorithms. Finally, we answer some open questions
regarding the complexity of -anonymity and -diversity left in the
literature.Comment: An extended abstract to appear in DASFAA 201
Quantum Optimization Problems
Krentel [J. Comput. System. Sci., 36, pp.490--509] presented a framework for
an NP optimization problem that searches an optimal value among
exponentially-many outcomes of polynomial-time computations. This paper expands
his framework to a quantum optimization problem using polynomial-time quantum
computations and introduces the notion of an ``universal'' quantum optimization
problem similar to a classical ``complete'' optimization problem. We exhibit a
canonical quantum optimization problem that is universal for the class of
polynomial-time quantum optimization problems. We show in a certain relativized
world that all quantum optimization problems cannot be approximated closely by
quantum polynomial-time computations. We also study the complexity of quantum
optimization problems in connection to well-known complexity classes.Comment: date change
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
Analysis of rolling group therapy data using conditionally autoregressive priors
Group therapy is a central treatment modality for behavioral health disorders
such as alcohol and other drug use (AOD) and depression. Group therapy is often
delivered under a rolling (or open) admissions policy, where new clients are
continuously enrolled into a group as space permits. Rolling admissions
policies result in a complex correlation structure among client outcomes.
Despite the ubiquity of rolling admissions in practice, little guidance on the
analysis of such data is available. We discuss the limitations of previously
proposed approaches in the context of a study that delivered group cognitive
behavioral therapy for depression to clients in residential substance abuse
treatment. We improve upon previous rolling group analytic approaches by fully
modeling the interrelatedness of client depressive symptom scores using a
hierarchical Bayesian model that assumes a conditionally autoregressive prior
for session-level random effects. We demonstrate improved performance using our
method for estimating the variance of model parameters and the enhanced ability
to learn about the complex correlation structure among participants in rolling
therapy groups. Our approach broadly applies to any group therapy setting where
groups have changing client composition. It will lead to more efficient
analyses of client-level data and improve the group therapy research
community's ability to understand how the dynamics of rolling groups lead to
client outcomes.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS434 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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